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针对傅里叶变换、小波算法等传统信号处理方法在非线性信号的提取与重构中存在的缺陷,提出了基于聚合经验模态分解的轧辊偏心信号提取新方法。另外,针对传统自动厚度控制系统(AGC)在偏心补偿控制中的不足,设计了有偏心补偿环节的AGC系统。新方法将轧制力信号分解为多个不同特征模态函数,从中提取表征偏心信号的特征模态函数,并用此重构偏心信号,最后将新方法重构的偏心信号投入到此系统中控制轧件厚度。仿真及实验结果表明,利用聚合经验模态分解方法重构得到的轧辊偏心模型可以很大程度减小厚度波动,补偿效果优于小波算法。
Aiming at the shortcomings of traditional signal processing methods such as Fourier transform and wavelet algorithm in the extraction and reconstruction of nonlinear signals, a new method of extracting eccentric roller signal based on the empirical mode decomposition is proposed. In addition, aiming at the deficiency of traditional automatic thickness control system (AGC) in eccentric compensation control, an AGC system with eccentric compensation is designed. The new method decomposes the rolling force signal into several different eigenmodel functions, extracts the eigenmodel function that characterizes the eccentricity signal and reconstructs the eccentricity signal. Finally, the eccentricity signal reconstructed by the new method is put into the system to control Rolling thickness. The simulation and experimental results show that the roll eccentricity model reconstructed by the polymerization empirical mode decomposition method can reduce the thickness fluctuation to a great extent, and the compensation effect is better than the wavelet algorithm.